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@article{200532,
        author = {T BALAKRISHNAN and ADITHYA NAMBIAR and AMAN KUMAR CHAUDHARY and BALAJI KANNAN},
        title = {Apex Analytics: F1 Circuit DNA Dashboard},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2399-2404},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200532},
        abstract = {This paper presents Apex Analytics, an interactive web-based Formula 1 analytics dashboard integrating the Rohan Rao Kaggle Dataset (1950–2024) with the FastF1 Python library (2018–2025) to deliver circuit-level insights unavailable in any existing public tool. The system’s central feature, Circuit DNA, provides a multi-layered analytical fingerprint for each Grand Prix circuit, covering constructor dominance trends, lap record progression, tyre strategy patterns, and driver telemetry including speed traces, throttle/brake profiles, and G-force data. Built on a Python FastAPI backend with eleven RESTful endpoints and a React.js 18 frontend using Chart.js, Three.js, and D3.js, the system includes an ML win-probability engine achieving 95.1% accuracy via Gradient Boosting. Validated across five circuits—Monaco, Silverstone, Monza, Spa-Francorchamps, and Suzuka—the system achieved 100% endpoint pass rates, sub-50ms simulation latency, and a user satisfaction score of 4.2/5.0. Apex Analytics is open-source, freely deployable, and requires no installation.},
        keywords = {Formula 1, sports analytics, data visualisation, telemetry, FastF1, Circuit DNA, machine learning, win prediction, React.js, FastAPI, Python, tyre strategy, interactive dashboard.},
        month = {May},
        }